← Entropy Atlas
Reproducible randomness
Monte Carlo estimation
Many sampled scenarios approximate quantities that are difficult to calculate directly.
Evaluation card
What “plenty” means here.
- Required property
- A generator with suitable statistical quality, a recorded seed, and enough independent-looking samples for the estimator.
- If it fails
- Correlation, short periods, or sampling mistakes create biased estimates and false confidence.
- Relevant attacker
- Usually no adversary; the risks are numerical error, model error, and irreproducible analysis.
- The Plenty Line
- Run convergence checks, independent streams, and sensitivity analyses; record generator, version, and seed.
- Why more is not automatically better
- More entropy does not remove Monte Carlo error; more well-designed samples often matter more.
Where it appears
Recognize the pattern.
- 01risk models
- 02physics integration
- 03uncertainty propagation
The Plentropy rule
Protect the requirement, then stop.
Run convergence checks, independent streams, and sensitivity analyses; record generator, version, and seed.
Once that claim has comfortable evidence and margin, improve the next limiting factor instead of worshipping a larger entropy number.